Pneumonia　diagnosis　based　on　CT　scans　is　crucial　for　the　effective　treatment.　Existing　deep　leanring-based　methods　mainly　focus　on　the　global　struction　of　the　whole　lung　organs,　while　ignore　the　information　of　local　detailed　lesions.　This　can　easily　lead　to　errors　in　pneumonia　decisions　and　a　decline　in　classification　accuracy.　Actully,　the　diagnosis　process　of　specialists　in　practice　involves　basically　two　steps　glancing　the　whole　lung　organs　to　capture　global　information　(global　view)　and　gazing　at　local　regions　for　observing　detailed　lesion　(local　view).　To　mimic　this　behaviour,　we　propose　a　multi-view　information　fusion　network　for　pneumonia　diagnosis　from　full　sequence　CTs.　First,　we　design　a　multi-view　information　fusion　network　to　extract　spatial　features　from　lung　CT　slices　from　global　and　local　perspectives.　Then,　a　recursive　neural　network　(RNN)　is　utilized　to　solve　the　problem　of　dependency　between　slices　and　the　continuity　of　lesions.　Extensive　experiments　on　real-world　datasets　are　conducted　and　the　results　demonstrate　the　effectiveness　of　our　proposed　method.　©　2023　IEEE.